Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations2144
Missing cells260
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory380.7 KiB
Average record size in memory181.8 B

Variable types

Text2
Numeric9

Alerts

athletes is highly overall correlated with events and 3 other fieldsHigh correlation
events is highly overall correlated with athletes and 3 other fieldsHigh correlation
height is highly overall correlated with weightHigh correlation
medals is highly overall correlated with athletes and 3 other fieldsHigh correlation
prev_3_medals is highly overall correlated with athletes and 3 other fieldsHigh correlation
prev_medals is highly overall correlated with athletes and 3 other fieldsHigh correlation
weight is highly overall correlated with heightHigh correlation
prev_medals has 130 (6.1%) missing valuesMissing
prev_3_medals has 130 (6.1%) missing valuesMissing
medals has 1282 (59.8%) zerosZeros
prev_medals has 1200 (56.0%) zerosZeros
prev_3_medals has 1001 (46.7%) zerosZeros

Reproduction

Analysis started2024-08-23 08:40:11.084178
Analysis finished2024-08-23 08:40:18.370199
Duration7.29 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

team
Text

Distinct219
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size109.0 KiB
2024-08-23T15:40:18.636285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6432
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG
ValueCountFrequency (%)
pur 14
 
0.7%
fin 14
 
0.7%
jam 14
 
0.7%
ven 14
 
0.7%
tto 14
 
0.7%
bel 14
 
0.7%
aut 14
 
0.7%
ita 14
 
0.7%
swe 14
 
0.7%
isl 14
 
0.7%
Other values (209) 2004
93.5%
2024-08-23T15:40:19.035266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 676
 
10.5%
R 534
 
8.3%
N 413
 
6.4%
U 378
 
5.9%
I 376
 
5.8%
S 373
 
5.8%
E 372
 
5.8%
M 354
 
5.5%
G 329
 
5.1%
L 312
 
4.9%
Other values (16) 2315
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 676
 
10.5%
R 534
 
8.3%
N 413
 
6.4%
U 378
 
5.9%
I 376
 
5.8%
S 373
 
5.8%
E 372
 
5.8%
M 354
 
5.5%
G 329
 
5.1%
L 312
 
4.9%
Other values (16) 2315
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 676
 
10.5%
R 534
 
8.3%
N 413
 
6.4%
U 378
 
5.9%
I 376
 
5.8%
S 373
 
5.8%
E 372
 
5.8%
M 354
 
5.5%
G 329
 
5.1%
L 312
 
4.9%
Other values (16) 2315
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 676
 
10.5%
R 534
 
8.3%
N 413
 
6.4%
U 378
 
5.9%
I 376
 
5.8%
S 373
 
5.8%
E 372
 
5.8%
M 354
 
5.5%
G 329
 
5.1%
L 312
 
4.9%
Other values (16) 2315
36.0%
Distinct235
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size121.0 KiB
2024-08-23T15:40:19.248720image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length32
Median length27
Mean length8.7509328
Min length4

Characters and Unicode

Total characters18762
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.9%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
and 54
 
2.0%
islands 49
 
1.8%
guinea 36
 
1.3%
united 33
 
1.2%
states 30
 
1.1%
republic 29
 
1.0%
south 25
 
0.9%
netherlands 25
 
0.9%
new 24
 
0.9%
korea 22
 
0.8%
Other values (263) 2438
88.2%
2024-08-23T15:40:19.572626image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2835
15.1%
i 1591
 
8.5%
n 1509
 
8.0%
e 1338
 
7.1%
r 1033
 
5.5%
o 977
 
5.2%
t 728
 
3.9%
l 722
 
3.8%
u 669
 
3.6%
s 634
 
3.4%
Other values (49) 6726
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2835
15.1%
i 1591
 
8.5%
n 1509
 
8.0%
e 1338
 
7.1%
r 1033
 
5.5%
o 977
 
5.2%
t 728
 
3.9%
l 722
 
3.8%
u 669
 
3.6%
s 634
 
3.4%
Other values (49) 6726
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2835
15.1%
i 1591
 
8.5%
n 1509
 
8.0%
e 1338
 
7.1%
r 1033
 
5.5%
o 977
 
5.2%
t 728
 
3.9%
l 722
 
3.8%
u 669
 
3.6%
s 634
 
3.4%
Other values (49) 6726
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2835
15.1%
i 1591
 
8.5%
n 1509
 
8.0%
e 1338
 
7.1%
r 1033
 
5.5%
o 977
 
5.2%
t 728
 
3.9%
l 722
 
3.8%
u 669
 
3.6%
s 634
 
3.4%
Other values (49) 6726
35.8%

year
Real number (ℝ)

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1994.5075
Minimum1964
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:19.676712image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1964
5-th percentile1968
Q11984
median1996
Q32008
95-th percentile2016
Maximum2016
Range52
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.384205
Coefficient of variation (CV)0.0077132853
Kurtosis-0.92952451
Mean1994.5075
Median Absolute Deviation (MAD)12
Skewness-0.39726161
Sum4276224
Variance236.67377
MonotonicityNot monotonic
2024-08-23T15:40:19.766571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2012 204
9.5%
2016 204
9.5%
2008 202
9.4%
2004 201
9.4%
2000 196
9.1%
1996 192
9.0%
1992 165
7.7%
1988 157
7.3%
1984 138
 
6.4%
1972 118
 
5.5%
Other values (4) 367
17.1%
ValueCountFrequency (%)
1964 91
4.2%
1968 105
4.9%
1972 118
5.5%
1976 92
4.3%
1980 79
3.7%
1984 138
6.4%
1988 157
7.3%
1992 165
7.7%
1996 192
9.0%
2000 196
9.1%
ValueCountFrequency (%)
2016 204
9.5%
2012 204
9.5%
2008 202
9.4%
2004 201
9.4%
2000 196
9.1%
1996 192
9.0%
1992 165
7.7%
1988 157
7.3%
1984 138
6.4%
1980 79
 
3.7%

events
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.724813
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:19.874637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median13
Q344
95-th percentile155
Maximum270
Range269
Interquartile range (IQR)38

Descriptive statistics

Standard deviation49.49027
Coefficient of variation (CV)1.3853192
Kurtosis4.5846954
Mean35.724813
Median Absolute Deviation (MAD)9
Skewness2.1878061
Sum76594
Variance2449.2868
MonotonicityNot monotonic
2024-08-23T15:40:19.999289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 145
 
6.8%
5 139
 
6.5%
7 110
 
5.1%
6 103
 
4.8%
3 100
 
4.7%
2 94
 
4.4%
8 83
 
3.9%
9 74
 
3.5%
10 65
 
3.0%
11 55
 
2.6%
Other values (206) 1176
54.9%
ValueCountFrequency (%)
1 31
 
1.4%
2 94
4.4%
3 100
4.7%
4 145
6.8%
5 139
6.5%
6 103
4.8%
7 110
5.1%
8 83
3.9%
9 74
3.5%
10 65
3.0%
ValueCountFrequency (%)
270 1
< 0.1%
265 1
< 0.1%
263 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%
254 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
245 2
0.1%
242 1
< 0.1%

athletes
Real number (ℝ)

HIGH CORRELATION 

Distinct367
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.693097
Minimum1
Maximum839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:20.118495image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median19
Q370.25
95-th percentile391.85
Maximum839
Range838
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation127.90758
Coefficient of variation (CV)1.7356792
Kurtosis7.7585281
Mean73.693097
Median Absolute Deviation (MAD)15
Skewness2.7326105
Sum157998
Variance16360.348
MonotonicityNot monotonic
2024-08-23T15:40:20.235914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 133
 
6.2%
5 115
 
5.4%
6 90
 
4.2%
3 90
 
4.2%
7 87
 
4.1%
2 86
 
4.0%
8 73
 
3.4%
9 69
 
3.2%
10 50
 
2.3%
11 42
 
2.0%
Other values (357) 1309
61.1%
ValueCountFrequency (%)
1 21
 
1.0%
2 86
4.0%
3 90
4.2%
4 133
6.2%
5 115
5.4%
6 90
4.2%
7 87
4.1%
8 73
3.4%
9 69
3.2%
10 50
 
2.3%
ValueCountFrequency (%)
839 1
< 0.1%
788 1
< 0.1%
764 1
< 0.1%
763 1
< 0.1%
734 1
< 0.1%
730 1
< 0.1%
726 1
< 0.1%
719 1
< 0.1%
715 1
< 0.1%
693 1
< 0.1%

age
Real number (ℝ)

Distinct160
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.778591
Minimum17
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:20.362280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20.8
Q123.275
median24.7
Q326.1
95-th percentile29
Maximum66
Range49
Interquartile range (IQR)2.825

Descriptive statistics

Standard deviation2.8085586
Coefficient of variation (CV)0.11334618
Kurtosis25.172173
Mean24.778591
Median Absolute Deviation (MAD)1.4
Skewness2.32727
Sum53125.3
Variance7.8880016
MonotonicityNot monotonic
2024-08-23T15:40:20.487311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 56
 
2.6%
24 55
 
2.6%
26 53
 
2.5%
23.5 51
 
2.4%
24.4 50
 
2.3%
24.5 44
 
2.1%
24.2 42
 
2.0%
25.8 40
 
1.9%
23.7 40
 
1.9%
24.7 40
 
1.9%
Other values (150) 1673
78.0%
ValueCountFrequency (%)
17 3
0.1%
17.3 1
 
< 0.1%
17.4 1
 
< 0.1%
17.5 1
 
< 0.1%
18 4
0.2%
18.2 2
0.1%
18.5 4
0.2%
18.6 2
0.1%
18.7 2
0.1%
18.8 2
0.1%
ValueCountFrequency (%)
66 1
< 0.1%
44.3 1
< 0.1%
43 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38.7 1
< 0.1%
38.5 1
< 0.1%
37.7 1
< 0.1%
36.7 1
< 0.1%
36.5 1
< 0.1%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct273
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.90023
Minimum151
Maximum193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:20.615509image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum151
5-th percentile164.215
Q1170.5
median174.4
Q3177.3
95-th percentile182
Maximum193
Range42
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation5.3573674
Coefficient of variation (CV)0.030807132
Kurtosis0.7562517
Mean173.90023
Median Absolute Deviation (MAD)3.3
Skewness-0.35947773
Sum372842.1
Variance28.701386
MonotonicityNot monotonic
2024-08-23T15:40:20.844912image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 32
 
1.5%
170 30
 
1.4%
176.5 29
 
1.4%
174 28
 
1.3%
174.2 26
 
1.2%
176.8 25
 
1.2%
175.3 24
 
1.1%
175.5 24
 
1.1%
176 24
 
1.1%
169 24
 
1.1%
Other values (263) 1878
87.6%
ValueCountFrequency (%)
151 1
 
< 0.1%
151.3 1
 
< 0.1%
154 1
 
< 0.1%
154.6 1
 
< 0.1%
155 1
 
< 0.1%
156 1
 
< 0.1%
156.8 1
 
< 0.1%
157 3
0.1%
157.2 1
 
< 0.1%
157.5 1
 
< 0.1%
ValueCountFrequency (%)
193 1
< 0.1%
191 1
< 0.1%
189.8 1
< 0.1%
189.7 1
< 0.1%
189.6 1
< 0.1%
189.1 1
< 0.1%
188.9 1
< 0.1%
188.6 1
< 0.1%
188 1
< 0.1%
187.7 1
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct354
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.271595
Minimum43
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:20.969376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile57.5
Q164.5
median69.4
Q373.4
95-th percentile81.285
Maximum148
Range105
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation7.6065068
Coefficient of variation (CV)0.10980701
Kurtosis7.1455167
Mean69.271595
Median Absolute Deviation (MAD)4.4
Skewness0.92006063
Sum148518.3
Variance57.858945
MonotonicityNot monotonic
2024-08-23T15:40:21.107555image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 24
 
1.1%
69.2 23
 
1.1%
69.5 22
 
1.0%
69.4 20
 
0.9%
68 20
 
0.9%
73 20
 
0.9%
73.5 18
 
0.8%
72.8 18
 
0.8%
72.6 18
 
0.8%
71.2 17
 
0.8%
Other values (344) 1944
90.7%
ValueCountFrequency (%)
43 1
< 0.1%
43.3 1
< 0.1%
47 1
< 0.1%
47.5 2
0.1%
48 1
< 0.1%
48.1 1
< 0.1%
48.3 1
< 0.1%
50 2
0.1%
50.2 1
< 0.1%
50.7 1
< 0.1%
ValueCountFrequency (%)
148 1
< 0.1%
117.7 1
< 0.1%
110.5 1
< 0.1%
108.5 1
< 0.1%
103 1
< 0.1%
100 1
< 0.1%
97.2 1
< 0.1%
96 2
0.1%
95.3 1
< 0.1%
94.6 1
< 0.1%

medals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.556437
Minimum0
Maximum442
Zeros1282
Zeros (%)59.8%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:21.235045image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile57.85
Maximum442
Range442
Interquartile range (IQR)4

Descriptive statistics

Standard deviation33.028143
Coefficient of variation (CV)3.1287208
Kurtosis43.004364
Mean10.556437
Median Absolute Deviation (MAD)0
Skewness5.7548377
Sum22633
Variance1090.8582
MonotonicityNot monotonic
2024-08-23T15:40:21.368283image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1282
59.8%
1 165
 
7.7%
2 83
 
3.9%
3 60
 
2.8%
5 40
 
1.9%
6 32
 
1.5%
4 32
 
1.5%
7 27
 
1.3%
8 23
 
1.1%
13 17
 
0.8%
Other values (123) 383
 
17.9%
ValueCountFrequency (%)
0 1282
59.8%
1 165
 
7.7%
2 83
 
3.9%
3 60
 
2.8%
4 32
 
1.5%
5 40
 
1.9%
6 32
 
1.5%
7 27
 
1.3%
8 23
 
1.1%
9 10
 
0.5%
ValueCountFrequency (%)
442 1
< 0.1%
352 1
< 0.1%
317 1
< 0.1%
300 1
< 0.1%
286 1
< 0.1%
264 2
0.1%
263 1
< 0.1%
259 1
< 0.1%
248 1
< 0.1%
242 1
< 0.1%

prev_medals
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct126
Distinct (%)6.3%
Missing130
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean10.248759
Minimum0
Maximum442
Zeros1200
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:21.492064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile57
Maximum442
Range442
Interquartile range (IQR)4

Descriptive statistics

Standard deviation31.95192
Coefficient of variation (CV)3.117638
Kurtosis46.307397
Mean10.248759
Median Absolute Deviation (MAD)0
Skewness5.8970046
Sum20641
Variance1020.9252
MonotonicityNot monotonic
2024-08-23T15:40:21.611948image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1200
56.0%
1 156
 
7.3%
2 77
 
3.6%
3 61
 
2.8%
5 39
 
1.8%
6 31
 
1.4%
4 31
 
1.4%
7 25
 
1.2%
8 16
 
0.7%
10 15
 
0.7%
Other values (116) 363
 
16.9%
(Missing) 130
 
6.1%
ValueCountFrequency (%)
0 1200
56.0%
1 156
 
7.3%
2 77
 
3.6%
3 61
 
2.8%
4 31
 
1.4%
5 39
 
1.8%
6 31
 
1.4%
7 25
 
1.2%
8 16
 
0.7%
9 12
 
0.6%
ValueCountFrequency (%)
442 1
< 0.1%
352 1
< 0.1%
317 1
< 0.1%
286 1
< 0.1%
264 1
< 0.1%
263 1
< 0.1%
259 1
< 0.1%
248 1
< 0.1%
242 1
< 0.1%
224 1
< 0.1%

prev_3_medals
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct222
Distinct (%)11.0%
Missing130
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean9.4499007
Minimum0
Maximum314
Zeros1001
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-08-23T15:40:21.727974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3
Q34.65
95-th percentile52
Maximum314
Range314
Interquartile range (IQR)4.65

Descriptive statistics

Standard deviation28.232227
Coefficient of variation (CV)2.9875686
Kurtosis37.863235
Mean9.4499007
Median Absolute Deviation (MAD)0.3
Skewness5.5050772
Sum19032.1
Variance797.05863
MonotonicityNot monotonic
2024-08-23T15:40:21.852943image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1001
46.7%
0.3 172
 
8.0%
0.7 62
 
2.9%
1 49
 
2.3%
1.3 34
 
1.6%
2 29
 
1.4%
1.7 27
 
1.3%
5 21
 
1.0%
3.3 20
 
0.9%
3 20
 
0.9%
Other values (212) 579
27.0%
(Missing) 130
 
6.1%
ValueCountFrequency (%)
0 1001
46.7%
0.3 172
 
8.0%
0.5 12
 
0.6%
0.7 62
 
2.9%
1 49
 
2.3%
1.3 34
 
1.6%
1.5 2
 
0.1%
1.7 27
 
1.3%
2 29
 
1.4%
2.3 18
 
0.8%
ValueCountFrequency (%)
314 1
< 0.1%
276 1
< 0.1%
274 1
< 0.1%
261 1
< 0.1%
254.7 1
< 0.1%
241.7 1
< 0.1%
241 1
< 0.1%
230.7 1
< 0.1%
230 1
< 0.1%
229 1
< 0.1%

Interactions

2024-08-23T15:40:17.196779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.252117image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.992978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.669631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.388029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.267735image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.148399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.835588image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.518379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.273130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.333881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.067861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.744451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.468721image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.346295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.229400image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.909117image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.592842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.352149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.473939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.143441image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.820321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.559167image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.422570image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.309754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.984540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.668663image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.428576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.547465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.216565image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.891517image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.637177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.498903image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.384094image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.055822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.742513image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.511158image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.619441image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.290780image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.964042image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.711782image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.731343image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.456792image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.133804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.819035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.589978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.690904image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.364640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.057060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.834245image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.808326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.531831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.210991image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.892481image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.678359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.769423image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.444131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.141463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.977457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.894451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.606645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.295376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.968334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.861725image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.838791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.514757image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.230940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.080950image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.976573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.681550image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.367898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.041583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.937499image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:11.912327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:12.589100image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:13.305247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:14.180961image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.069882image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:15.756302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:16.440346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-23T15:40:17.114106image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-23T15:40:21.937796image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ageathleteseventsheightmedalsprev_3_medalsprev_medalsweightyear
age1.0000.1320.1340.2210.1510.1780.1650.2730.081
athletes0.1321.0000.9790.2970.7980.8130.7820.238-0.143
events0.1340.9791.0000.2710.7890.8070.7770.228-0.101
height0.2210.2970.2711.0000.2640.2630.2490.7470.009
medals0.1510.7980.7890.2641.0000.8260.8330.2180.002
prev_3_medals0.1780.8130.8070.2630.8261.0000.9170.221-0.040
prev_medals0.1650.7820.7770.2490.8330.9171.0000.209-0.023
weight0.2730.2380.2280.7470.2180.2210.2091.0000.023
year0.081-0.143-0.1010.0090.002-0.040-0.0230.0231.000

Missing values

2024-08-23T15:40:18.052523image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-23T15:40:18.208319image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-23T15:40:18.324200image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

teamcountryyeareventsathletesageheightweightmedalsprev_medalsprev_3_medals
0AFGAfghanistan19648822.0161.064.200.00.0
1AFGAfghanistan19685523.2170.270.000.00.0
2AFGAfghanistan19728829.0168.363.800.00.0
3AFGAfghanistan1980111123.6168.463.200.00.0
4AFGAfghanistan20045518.6170.864.800.00.0
5AFGAfghanistan20084422.5179.262.810.00.0
6AFGAfghanistan20126624.8171.760.811.00.3
7AFGAfghanistan20163324.7173.774.001.00.7
8AHONetherlands Antilles19644428.5171.269.400.00.0
9AHONetherlands Antilles19684531.0173.267.800.00.0
teamcountryyeareventsathletesageheightweightmedalsprev_medalsprev_3_medals
2134ZIMZimbabwe1980305226.9171.971.0150.00.0
2135ZIMZimbabwe1984182029.1174.067.6015.05.0
2136ZIMZimbabwe1988384725.1176.570.300.05.0
2137ZIMZimbabwe1992222821.2171.162.400.05.0
2138ZIMZimbabwe1996152123.8176.768.700.00.0
2139ZIMZimbabwe2000192625.0179.071.100.00.0
2140ZIMZimbabwe2004111425.1177.870.530.00.0
2141ZIMZimbabwe2008151626.1171.963.743.01.0
2142ZIMZimbabwe20128927.3174.465.204.02.3
2143ZIMZimbabwe2016133127.5167.862.200.02.3